arr2int                 Convert a vector of integers to a compact
                        interval string
as.data.frame.ddsimcares
                        Creates a data frame from DD-SIMCA
                        classification results.
as.matrix.classres      as.matrix method for classification results
as.matrix.ddsimcares    Creates a matrix from DD-SIMCA classification
                        results.
as.matrix.ldecomp       as.matrix method for ldecomp object
as.matrix.plsdares      as.matrix method for PLS-DA results
as.matrix.plsres        as.matrix method for PLS results
as.matrix.regcoeffs     as.matrix method for regression coefficients
                        class
as.matrix.regres        as.matrix method for regression results
as.matrix.simcamres     as.matrix method for SIMCAM results
as.matrix.simcares      as.matrix method for SIMCA classification
                        results
asjson                  S3 implementation of asjson() method
asjson.ddsimca          Converts object with DD-SIMCA model to JSON
                        string compatible with web-application.
asjson.pca              Converts object with PCA model to JSON string
                        compatible with web-application.
asjson.pls              Converts object with PLS model to JSON string
                        compatible with web-application.
asvector                S3 implementation of as.vector() method
asvector.pca            Converts object with PCA model to numeric
                        vector compatible with web-application.
asvector.pls            Converts object with PLS model to numeric
                        vector compatible with web-application.
capitalize              Capitalize text or vector with text values
carbs                   Raman spectra of carbonhydrates
categorize              Categorize PCA results
categorize.pca          Categorize PCA results based on orthogonal and
                        score distances.
categorize.pls          Categorize data rows based on PLS results and
                        critical limits for total distance.
chisq.crit              Calculates critical limits for distance values
                        using Chi-square distribution
chisq.prob              Calculate probabilities for distance values
                        using Chi-square distribution
clamp.dof               Round and clamp degrees of freedom to valid
                        range [1, 250]
classify                Creates classification outcomes for given PCA
                        result objects and distance parameters.
classify.plsda          PLS-DA classification
classify.simca          SIMCA classification
classmodel.processRefValues
                        Check reference class values and convert it to
                        a factor if necessary
classres                Results of classification
classres.getPerformance
                        Calculation of classification performance
                        parameters
cleanLabels             Clean text labels from extra elements so they
                        are compatible with JSON
confint.regcoeffs       Confidence intervals for regression
                        coefficients
constraint              Class for MCR-ALS constraint
constraintAngle         Method for angle constraint
constraintClosure       Method for closure constraint
constraintNonNegativity
                        Method for non-negativity constraint
constraintNorm          Method for normalization constraint
constraintUnimod        Method for unimodality constraint
constraints.list        Shows information about all implemented
                        constraints
create_categories       Create a factor with categories (regular,
                        extreme, outlier)
crossval                Generate sequence of indices for
                        cross-validation
crossval.getParams      Define parameters based on 'cv' value
crossval.regmodel       Cross-validation of a regression model
crossval.simca          Cross-validation of a SIMCA model
crossval.str            String with description of cross-validation
                        method
dd.crit                 Calculates critical limits for distance values
                        using Data Driven moments approach
ddmoments.param         Calculates critical limits for distance values
                        using Data Driven moments approach
ddrobust.param          Calculates critical limits for distance values
                        using Data Driven robust approach
ddsimca                 Data Driven SIMCA
ddsimca.fromjson        Converts JSON string created in
                        mda.tools/ddsimca app to 'ddsimca' object
ddsimca.readJSON        Reads DD-SIMCA model from JSON file made in
                        web-application (mda.tools/ddsimca).
ddsimcares              Results of DD-SIMCA one-class classification
ellipse                 Create ellipse on the current plot
employ.constraint       Applies constraint to a dataset
employ.prep             Applies a list with preprocessing methods to a
                        dataset
extractArray            Extract numeric array from JSON string
extractBlock            Extracts a JSON subset in main JSON structure
extractPrep             Extracts JSON related to preprocessing model
extractStringArray      Extract string array from JSON string
extractValue            Extract single value from JSON string
fprintf                 Imitation of fprintf() function
genhash                 Generates unique pseudo-hash number based on
                        current time and date
getCalibrationData      Calibration data
getCalibrationData.pca
                        Returns matrix with original calibration data
getCalibrationData.simcam
                        Get calibration data
getConfidenceEllipse    Compute confidence ellipse for a set of points
getConfusionMatrix      Confusion matrix for classification results
getConfusionMatrix.classres
                        Confusion matrix for classification results
getConvexHull           Compute coordinates of a closed convex hull for
                        data points
getDataLabels           Create a vector with labels for plot series
getImplementedConstraints
                        Shows a list with implemented constraints
getImplementedPrepMethods
                        Shows a list with implemented preprocessing
                        methods
getLabelsAsIndices      Create labels as column or row indices
getLabelsAsValues       Create labels from data values
getMainTitle            Get main title
getPlotColors           Define colors for plot series
getProbabilities        Get class belonging probability
getProbabilities.pca    Probabilities for residual distances
getProbabilities.simca
                        Probabilities of class belonging for PCA/SIMCA
                        results
getPureVariables        Identifies pure variables
getRegcoeffs            Get regression coefficients
getRegcoeffs.regmodel   Regression coefficients for PLS model
getRes                  Return list with valid results
getSelectedComponents   Get selected components
getSelectivityRatio     Selectivity ratio
getSelectivityRatio.pls
                        Selectivity ratio for PLS model
getVIPScores            VIP scores
getVIPScores.pls        VIP scores for PLS model
getVariance.mcr         Compute explained variance for MCR case
hotelling.crit          Calculate critical limits for distance values
                        using Hotelling T2 distribution
hotelling.prob          Calculate probabilities for distance values and
                        given parameters using Hotelling T2
                        distribution
imshow                  show image data as an image
ipls                    Variable selection with interval PLS
ipls.backward           Runs the backward iPLS algorithm
ipls.forward            Runs the forward iPLS algorithm
jm.crit                 Calculate critical limits for distance values
                        using Jackson-Mudholkar approach
jm.prob                 Calculate probabilities for distance values and
                        given parameters using Hotelling T2
                        distribution
ldecomp                 Class for storing and visualising linear
                        decomposition of dataset (X = TP' + E)
ldecomp.getDistances    Compute score and residual distances
ldecomp.getLimParams    Compute parameters for critical limits based on
                        calibration results
ldecomp.getLimitsCoordinates
                        Compute coordinates of lines or curves with
                        critical limits
ldecomp.getQLimits      Compute critical limits for orthogonal
                        distances (Q)
ldecomp.getT2Limits     Compute critical limits for score distances
                        (T2)
ldecomp.getVariances    Compute explained variance
ldecomp.plotDistances   Distance plot for a set of ldecomp objects
ldecomp.plotResiduals   Residuals distance plot for a set of ldecomp
                        objects (legacy, use 'ldecomp.plotDistances'
                        instead).
mcr                     General class for Multivariate Curve Resolution
                        model
mcrals                  Multivariate curve resolution using Alternating
                        Least Squares
mcrals.cal              MCR-ALS calibration
mcrals.fcnnls           Fast combinatorial non-negative least squares
mcrals.nnls             Non-negative least squares
mcrals.ols              Ordinary least squares
mcrpure                 Multivariate curve resolution based on pure
                        variables
mda.cbind               A wrapper for cbind() method with proper set of
                        attributes
mda.data2im             Convert data matrix to an image
mda.df2mat              Convert data frame to a matrix
mda.exclcols            Exclude/hide columns in a dataset
mda.exclrows            Exclude/hide rows in a dataset
mda.getattr             Get data attributes
mda.getexclind          Get indices of excluded rows or columns
mda.im2data             Convert image to data matrix
mda.inclcols            Include/unhide the excluded columns
mda.inclrows            include/unhide the excluded rows
mda.purge               Removes excluded (hidden) rows and columns from
                        data
mda.purgeCols           Removes excluded (hidden) columns from data
mda.purgeRows           Removes excluded (hidden) rows from data
mda.rbind               A wrapper for rbind() method with proper set of
                        attributes
mda.setattr             Set data attributes
mda.setimbg             Remove background pixels from image data
mda.show                Wrapper for show() method
mda.subset              A wrapper for subset() method with proper set
                        of attributes
mda.t                   A wrapper for t() method with proper set of
                        attributes
mdaplot                 Plotting function for a single set of objects
mdaplot.areColors       Check color values
mdaplot.formatValues    Format vector with numeric values
mdaplot.getColors       Color values for plot elements
mdaplot.getXAxisLim     Calculate limits for x-axis.
mdaplot.getXTickLabels
                        Prepare xticklabels for plot
mdaplot.getXTicks       Prepare xticks for plot
mdaplot.getYAxisLim     Calculate limits for y-axis.
mdaplot.getYTickLabels
                        Prepare yticklabels for plot
mdaplot.getYTicks       Prepare yticks for plot
mdaplot.plotAxes        Create axes plane
mdaplot.prepareColors   Prepare colors based on palette and opacity
                        value
mdaplot.showColorbar    Plot colorbar
mdaplot.showLines       Plot lines
mdaplotg                Plotting function for several plot series
mdaplotg.getLegend      Create and return vector with legend values
mdaplotg.getXLim        Compute x-axis limits for mdaplotg
mdaplotg.getYLim        Compute y-axis limits for mdaplotg
mdaplotg.prepareData    Prepare data for mdaplotg
mdaplotg.processParam   Check mdaplotg parameters and replicate them if
                        necessary
mdaplotg.showLegend     Show legend for mdaplotg
mdaplotyy               Create line plot with double y-axis
mdatools                Package for Multivariate Data Analysis
                        (Chemometrics)
paste1                  Paste values together with no separator and
                        collapse into a single string
pca                     Principal Component Analysis
pca.cal                 PCA model calibration
pca.fromjson            Converts JSON string created in mda.tools/pca
                        app to 'pca' object
pca.getB                Low-dimensional approximation of data matrix X
pca.mvreplace           Replace missing values in data
pca.nipals              NIPALS based PCA algorithm
pca.readJSON            Reads PCA model from JSON file made in
                        web-application (mda.tools/pca).
pca.run                 Runs one of the selected PCA methods
pca.svd                 Singular Values Decomposition based PCA
                        algorithm
pca.syncResAliases      Sync calres/testres aliases with the canonical
                        res[["cal"]]/res[["test"]] fields.
pcares                  Results of PCA decomposition
pellets                 Image data
people                  People data
pinv                    Pseudo-inverse matrix
plot.classres           Plot function for classification results
plot.ddsimca            Model overview plot for DD-SIMCA
plot.ddsimcares         Plot method for DD-SIMCA results.
plot.ipls               Overview plot for iPLS results
plot.mcr                Plot summary for MCR model
plot.pca                Model overview plot for PCA
plot.pcares             Plot method for PCA results object
plot.pls                Model overview plot for PLS
plot.plsda              Model overview plot for PLS-DA
plot.plsdares           Overview plot for PLS-DA results
plot.plsres             Overview plot for PLS results
plot.randtest           Plot for randomization test results
plot.regcoeffs          Regression coefficients plot
plot.regres             Plot method for regression results
plot.simca              Model overview plot for SIMCA
plot.simcam             Model overview plot for SIMCAM
plot.simcamres          Model overview plot for SIMCAM results
plotAcceptance          Acceptance plot for DDSIMCA model and results
                        (generic function)
plotAcceptance.ddsimca
                        Acceptance plot for DD-SIMCA model.
plotAcceptance.ddsimcares
                        Acceptance plot for DD-SIMCA results object.
plotAliens              Aliens plot for DD-SIMCA results (generic
                        function)
plotAliens.ddsimcares   Aliens plot for DD-SIMCA results.
plotBars                Show plot series as bars
plotBiplot              Biplot
plotBiplot.pca          PCA biplot
plotConfidenceEllipse   Add confidence ellipse for groups of points on
                        scatter plot
plotContributions       Plot resolved contributions
plotContributions.mcr   Show plot with resolved contributions
plotConvexHull          Add convex hull for groups of points on scatter
                        plot
plotCooman              Cooman's plot
plotCooman.simcam       Cooman's plot for SIMCAM model
plotCooman.simcamres    Cooman's plot for SIMCAM results
plotCorr                Correlation plot
plotCorr.randtest       Correlation plot for randomization test results
plotCumVariance         Variance plot
plotCumVariance.ldecomp
                        Cumulative explained variance plot
plotCumVariance.mcr     Show plot with cumulative explained variance
plotCumVariance.pca     Cumulative explained variance plot for PCA
                        model
plotDensity             Show plot series as density plot (using hex
                        binning)
plotDiscriminationPower
                        Discrimination power plot
plotDiscriminationPower.simcam
                        Discrimination power plot for SIMCAM model
plotDistDoF             Degrees of freedom plot for both distances
plotDistances           Distance plot for model and results (generic
                        function)
plotDistances.ddsimca   Show with distance values (score, orthogonal or
                        full) vs object indices for calibration and
                        PV-set results.
plotDistances.ddsimcares
                        Show with distance values (score, orthogonal or
                        full) vs object indices for DD-SIMCA results.
plotDistances.ldecomp   Distance plot
plotDistances.pca       Distance plot for PCA model
plotEigenvalues         Eigenvalues plot
plotEigenvalues.pca     Eigenvalues plot for PCA model
plotErrorbars           Show plot series as error bars
plotExtreme             Shows extreme plot for PCA and DD-SIMCA models
plotExtreme.ddsimca     A shortcut to 'plotExtremes.ddsimca'.
plotExtreme.ddsimcares
                        Extremes plot (shortcut to
                        'plotExtremes.ddsimcares').
plotExtreme.pca         A shortcut to ''plotExtremes.pca'.
plotExtremes            Shows extreme plot for PCA and DD-SIMCA models
plotExtremes.ddsimca    Extreme plot
plotExtremes.ddsimcares
                        Extremes plot.
plotExtremes.pca        Extreme plot
plotFoM                 Show plot with figure of merit vs. number of
                        components (generic function).
plotFoM.ddsimcares      Figure of merit plot.
plotFoMs                Show plot with several figures of merit vs.
                        number of components (generic function).
plotFoMs.ddsimcares     Figures of merit plot (multiple FoMs).
plotHist                Statistic histogram
plotHist.randtest       Histogram plot for randomization test results
plotHotellingEllipse    Hotelling ellipse
plotLines               Show plot series as set of lines
plotLoadings            Loadings plot
plotLoadings.pca        Loadings plot for PCA model
plotMisclassified       Misclassification ratio plot
plotMisclassified.classmodel
                        Misclassified ratio plot for classification
                        model
plotMisclassified.classres
                        Misclassified ratio plot for classification
                        results
plotModelDistance       Model distance plot
plotModelDistance.simcam
                        Model distance plot for SIMCAM model
plotModellingPower      Modelling power plot
plotPerformance         Classification performance plot
plotPerformance.classmodel
                        Performance plot for classification model
plotPerformance.classres
                        Performance plot for classification results
plotPointsShape         Add confidence ellipse or convex hull for group
                        of points
plotPredictions         Predictions plot
plotPredictions.classmodel
                        Predictions plot for classification model
plotPredictions.classres
                        Prediction plot for classification results
plotPredictions.regmodel
                        Predictions plot for regression model
plotPredictions.regres
                        Predictions plot for regression results
plotPredictions.simcam
                        Predictions plot for SIMCAM model
plotPredictions.simcamres
                        Prediction plot for SIMCAM results
plotProbabilities       Plot for class belonging probability
plotProbabilities.classres
                        Plot for class belonging probability
plotPurity              Plot purity values
plotPurity.mcrpure      Purity values plot
plotPuritySpectra       Plot purity spectra
plotPuritySpectra.mcrpure
                        Purity spectra plot
plotQDoF                Degrees of freedom plot for orthogonal distance
                        (Nq)
plotRMSE                RMSE plot
plotRMSE.ipls           RMSE development plot
plotRMSE.regmodel       RMSE plot for regression model
plotRMSE.regres         RMSE plot for regression results
plotRMSERatio           Plot for ratio RMSEC/RMSECV vs RMSECV
plotRMSERatio.regmodel
                        RMSECV/RMSEC ratio plot for regression model
plotRegcoeffs           Regression coefficients plot
plotRegcoeffs.regmodel
                        Regression coefficient plot for regression
                        model
plotRegressionLine      Add regression line for data points
plotResiduals           Residuals plot
plotResiduals.ldecomp   Residuals distance plot for a set of ldecomp
                        objects (legacy, use 'plotDistances.ldecomp'
                        instead).
plotResiduals.pca       Residuals distance plot for PCA model (legacy,
                        use 'plotResiduals' instead).
plotResiduals.regres    Residuals plot for regression results
plotScatter             Show plot series as set of points
plotScores              Scores plot
plotScores.ldecomp      Scores plot
plotScores.pca          Scores plot for PCA model
plotSelection           Selected intervals plot
plotSelection.ipls      iPLS performance plot
plotSelectivityArea     Selectivity vs sensitivity plot for DD-SIMCA
                        results (generic function)
plotSelectivityArea.ddsimcares
                        Selectivity area plot (similar to ROC curve).
plotSelectivityRatio    Selectivity ratio plot
plotSelectivityRatio.pls
                        Selectivity ratio plot for PLS model
plotSensitivity         Sensitivity plot
plotSensitivity.classmodel
                        Sensitivity plot for classification model
plotSensitivity.classres
                        Sensitivity plot for classification results
plotSensitivity.ddsimca
                        Sensitivity plot.
plotSensitivity.ddsimcares
                        Sensitivity plot.
plotSpecificity         Specificity plot
plotSpecificity.classmodel
                        Specificity plot for classification model
plotSpecificity.classres
                        Specificity plot for classification results
plotSpectra             Plot resolved spectra
plotSpectra.mcr         Show plot with resolved spectra
plotT2DoF               Degrees of freedom plot for score distance (Nh)
plotVIPScores           VIP scores plot
plotVIPScores.pls       VIP scores plot for PLS model
plotVariance            Variance plot
plotVariance.ldecomp    Explained variance plot
plotVariance.mcr        Show plot with explained variance
plotVariance.pca        Explained variance plot for PCA model
plotVariance.pls        Variance plot for PLS
plotVariance.plsres     Explained X variance plot for PLS results
plotWeights             Plot for PLS weights
plotWeights.pls         Weights plot for PLS
plotXCumVariance        X cumulative variance plot
plotXCumVariance.pls    Cumulative explained X variance plot for PLS
plotXCumVariance.plsres
                        Explained cumulative X variance plot for PLS
                        results
plotXLoadings           X loadings plot
plotXLoadings.pls       X loadings plot for PLS
plotXResiduals          X residuals plot
plotXResiduals.pls      Residual distance plot for decomposition of X
                        data
plotXResiduals.plsres   X residuals plot for PLS results
plotXScores             X scores plot
plotXScores.pls         X scores plot for PLS
plotXScores.plsres      X scores plot for PLS results
plotXVariance           X variance plot
plotXVariance.pls       Explained X variance plot for PLS
plotXVariance.plsres    Explained X variance plot for PLS results
plotXYLoadings          XY loadings plot
plotXYLoadings.pls      XY loadings plot for PLS
plotXYResiduals         Plot for XY-residuals
plotXYResiduals.pls     Residual XY-distance plot
plotXYResiduals.plsres
                        Residual distance plot
plotXYScores            XY scores plot
plotXYScores.pls        XY scores plot for PLS
plotXYScores.plsres     XY scores plot for PLS results
plotYCumVariance        Y cumulative variance plot
plotYCumVariance.pls    Cumulative explained Y variance plot for PLS
plotYCumVariance.plsres
                        Explained cumulative Y variance plot for PLS
                        results
plotYResiduals          Y residuals plot
plotYResiduals.plsres   Y residuals plot for PLS results
plotYResiduals.regmodel
                        Y residuals plot for regression model
plotYVariance           Y variance plot
plotYVariance.pls       Explained Y variance plot for PLS
plotYVariance.plsres    Explained Y variance plot for PLS results
plotseries              Create plot series object based on data, plot
                        type and parameters
pls                     Partial Least Squares regression
pls.cal                 PLS model calibration
pls.fromjson            Converts JSON string created in mda.tools/pls
                        app to 'pls' object
pls.getLimitsCoordinates
                        Compute coordinates of lines or curves with
                        critical limits
pls.getZLimits          Compute critical limits for orthogonal
                        distances (Q)
pls.getpredictions      Compute predictions for response values
pls.getxdecomp          Compute object with decomposition of x-values
pls.getxscores          Compute matrix with X-scores
pls.getydecomp          Compute object with decomposition of y-values
pls.getyscores          Compute and orthogonalize matrix with Y-scores
pls.readJSON            Reads PLS model from JSON file made in
                        web-application (mda.tools/pls).
pls.run                 Runs selected PLS algorithm
pls.simpls              SIMPLS algorithm
pls.syncResAliases      Sync result aliases (calres, cvres, testres)
                        from canonical res list
plsda                   Partial Least Squares Discriminant Analysis
plsdares                PLS-DA results
plsres                  PLS results
predict.ddsimca         DD-SIMCA predictions
predict.mcrals          MCR ALS predictions
predict.mcrpure         MCR predictions
predict.pca             PCA predictions
predict.pls             PLS predictions
predict.plsda           PLS-DA predictions
predict.simca           SIMCA predictions
predict.simcam          SIMCA multiple classes predictions
prep                    Class for preprocessing object/item.
prep.alsbasecorr        Baseline correction using asymmetric least
                        squares
prep.alsbasecorr.asjson
                        Converts preprocessing item from
                        'prep.alsbasecorr' method to JSON elements
prep.alsbasecorr.fromjson
                        Converts JSON elements to preprocessing item
                        for 'prep.alsbasecorr' method
prep.apply              Applies a list with preprocessing methods to a
                        dataset
prep.asjson             Converts preprocessing model to JSON elements.
prep.autoscale          Autoscale values
prep.center             Centering data columns.
prep.center.asjson      Converts preprocessing item from 'prep.center'
                        method to JSON elements
prep.center.fromjson    Converts JSON elements to preprocessing item
                        for 'prep.center' method
prep.center.params      Precomputes parameters for centering
prep.emsc               Applies Extended Multiplicative Scatter
                        Correction to data rows
prep.emsc.asjson        Converts preprocessing item from 'prep.emsc'
                        method to JSON elements
prep.emsc.fromjson      Converts JSON elements to preprocessing item
                        for 'prep.emsc' method
prep.emsc.params        Precomputes parameters for EMSC
prep.fit                Fits preprocessing model
prep.fromjson           Converts JSON string to preprocessing model
prep.generic            Generic function for preprocessing
prep.list               Shows information about all implemented
                        preprocessing methods.
prep.msc                Multiplicative Scatter Correction
                        transformation
prep.norm               Normalization
prep.norm.asjson        Converts preprocessing item from 'prep.norm'
                        method to JSON elements
prep.norm.fromjson      Converts JSON elements to preprocessing item
                        for 'prep.norm' method
prep.norm.params        Precomputes parameters for normalization
prep.ref2km             Kubelka-Munk transformation
prep.savgol             Savitzky-Golay filter
prep.savgol.asjson      Converts preprocessing item from 'prep.savgol'
                        method to JSON elements
prep.savgol.fromjson    Converts JSON elements to preprocessing item
                        for 'prep.savgol' method
prep.savgol.params      Precomputes parameters for Savitzky-Golay
prep.scale              Scaling data columns.
prep.scale.asjson       Converts preprocessing item from 'prep.scale'
                        method to JSON elements
prep.scale.fromjson     Converts JSON elements to preprocessing item
                        for 'prep.scale' method
prep.scale.params       Precomputes parameters for scaling
prep.snv                Standard Normal Variate transformation
prep.spikes             Remove spikes from Raman spectra
prep.spikes.asjson      Converts preprocessing item from 'prep.spikes'
                        method to JSON elements
prep.spikes.fromjson    Converts JSON elements to preprocessing item
                        for 'prep.spikes' method
prep.transform          Transformation
prep.varsel             Variable selection
prep.varsel.asjson      Converts preprocessing item from 'prep.varsel'
                        method to JSON elements
prep.varsel.fromjson    Converts JSON elements to preprocessing item
                        for 'prep.varsel' method
prepCalData             Prepares calibration data
preparePlotData         Take dataset and prepare them for plot
print.classres          Print information about classification result
                        object
print.ddsimca           Print method for DD-SIMCA model object
print.ddsimcares        Print method for DD-SIMCA results
print.ipls              Print method for iPLS
print.ldecomp           Print method for linear decomposition
print.mcrals            Print method for mcrals object
print.mcrpure           Print method for mcrpure object
print.pca               Print method for PCA model object
print.pcares            Print method for PCA results object
print.pls               Print method for PLS model object
print.plsda             Print method for PLS-DA model object
print.plsdares          Print method for PLS-DA results object
print.plsres            print method for PLS results object
print.prepmodel         Print the information about methods in the
                        preprocessing model.
print.randtest          Print method for randtest object
print.regcoeffs         print method for regression coefficients class
print.regmodel          Print method for regression model object
print.regres            print method for regression results object
print.simca             Print method for SIMCA model object
print.simcam            Print method for SIMCAM model object
print.simcamres         Print method for SIMCAM results object
print.simcares          Print method for SIMCA results object
processLimType          Make correction to limit types names.
processMembers          Computes classification outcomes for target
                        class members.
processStrangers        Computes classification outcomes for members of
                        non-target classes.
randtest                Randomization test for PLS regression
readJSON                Reads models from JSON file made in
                        web-application (mda.tools).
regcoeffs               Regression coefficients
regcoeffs.getStats      Distribution statistics for regression
                        coefficients
regres                  Regression results
regres.bias             Prediction bias
regres.err              Error of prediction
regres.r2               Determination coefficient
regres.rmse             RMSE
regres.slope            Slope
regress.addattrs        Add names and attributes to matrix with
                        statistics
repmat                  Replicate matrix x
selectCompNum           Select optimal number of components for a model
selectCompNum.pca       Select optimal number of components for PCA
                        model
selectCompNum.pls       Select optimal number of components for PLS
                        model
selratio                Selectivity ratio calculation
setDistanceLimits       Set residual distance limits
setDistanceLimits.pca   Compute and set statistical limits for Q and T2
                        residual distances.
setDistanceLimits.pls   Compute and set statistical limits for residual
                        distances.
setParams               Set model parameters other than number of
                        components (generic function)
setParams.ddsimca       Set default parameters for the DD-SIMCA model.
showDistanceLimits      Show residual distance limits
showLabels              Show labels on plot
showPredictions         Predictions
showPredictions.classres
                        Show predicted class values
simca                   SIMCA one-class classification
simcam                  SIMCA multiclass classification
simcam.getPerformanceStats
                        Performance statistics for SIMCAM model
simcamres               Results of SIMCA multiclass classification
simcares                Results of SIMCA one-class classification
simdata                 Spectral data of polyaromatic hydrocarbons
                        mixing
splitExcludedData       Split the excluded part of data
splitPlotData           Split dataset to x and y values depending on
                        plot type
summary.classres        Summary statistics about classification result
                        object
summary.ddsimca         Summary method for DD-SIMCA model object
summary.ddsimcares      Summary method for DD-SIMCA results.
summary.ipls            Summary for iPLS results
summary.ldecomp         Summary statistics for linear decomposition
summary.mcrals          Summary method for mcrals object
summary.mcrpure         Summary method for mcrpure object
summary.pca             Summary method for PCA model object
summary.pcares          Summary method for PCA results object
summary.pls             Summary method for PLS model object
summary.plsda           Summary method for PLS-DA model object
summary.plsdares        Summary method for PLS-DA results object
summary.plsres          summary method for PLS results object
summary.prepmodel       Show summary of the preprocessing model.
summary.randtest        Summary method for randtest object
summary.regcoeffs       Summary method for regcoeffs object
summary.regmodel        Summary method for regression model object
summary.regres          summary method for regression results object
summary.simca           Summary method for SIMCA model object
summary.simcam          Summary method for SIMCAM model object
summary.simcamres       Summary method for SIMCAM results object
summary.simcares        Summary method for SIMCA results object
unmix.mcrpure           Unmix spectral data using pure variables
                        estimated before
vipscores               VIP scores for PLS model
writeCSV                Method to write outcomes of any result object
                        to CSV file
writeCSV.ddsimcares     Save DD-SIMCA results to CSV file
writeCSV.pcares         Save PCA results to CSV file
writeCSV.plsres         Save PLS results to CSV file
writeJSON               Save model as JSON file
writeJSON.ddsimca       Saves DD-SIMCA model as JSON file compatible
                        with web-application
                        (https://mda.tools/ddsimca).
writeJSON.pca           Saves PCA model as JSON file compatible with
                        web-application (https://mda.tools/pca).
writeJSON.pls           Saves PLS model as JSON file compatible with
                        web-application (https://mda.tools/pls).
writeJSON.prepmodel     Saves preprocessing model to JSON file which
                        can be loaded to web-application
                        (mda.tools/prep).
